The integration of machine learning techniques into microstructure design and the prediction of material properties has ushered in a transformative era for materials science. By leveraging advanced ...

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integral to database management, driving new levels of automation and intelligence in how data systems are administered. Modern ...

Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and microfluidics to nanomaterial testing, thanks to their compact size and ...

Below is a curated list of machine learning development providers that stand out in 2026 for their ability to build enterprise-grade ML solutions tailored to complex business environments.

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Xanadu, a global leader in quantum computing software and quantum-photonic hardware, today announced a new research initiative with Lockheed ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

A recent study suggests that a freely available AI tool could help predict dangerous complications after stem cell transplants.